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dc.contributor.authorSharma, Aloken_US
dc.contributor.authorPujari, Arunen_US
dc.contributor.authorPaliwal, Kuldipen_US
dc.date.accessioned2017-05-03T13:01:11Z
dc.date.available2017-05-03T13:01:11Z
dc.date.issued2007en_US
dc.date.modified2009-09-21T05:47:47Z
dc.identifier.issn01674048en_US
dc.identifier.doi10.1016/j.cose.2007.10.003en_AU
dc.identifier.urihttp://hdl.handle.net/10072/17394
dc.description.abstractThis paper focuses on intrusion detection based on system call sequences using text processing techniques. It introduces kernel based similarity measure for the detection of host-based intrusions. The k-nearest neighbour (kNN) classifier is used to classify a process as either normal or abnormal. The proposed technique is evaluated on the DARPA-1998 database and its performance is compared with other existing techniques available in the literature. It is shown that this technique is significantly better than the other techniques in achieving lower false positive rates at 100% detection rate.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherElsevieren_US
dc.publisher.placeNetherlandsen_US
dc.publisher.urihttp://www.elsevier.com/wps/find/journaldescription.cws_home/405877/description#descriptionen_AU
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom488en_US
dc.relation.ispartofpageto495en_US
dc.relation.ispartofjournalComputers & Securityen_US
dc.relation.ispartofvolume26en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode280207en_US
dc.titleIntrusion detection using text processing techniques with a kernel based similarity measureen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Sciences, Griffith School of Engineeringen_US
gro.date.issued2007
gro.hasfulltextNo Full Text


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